Condition maintenance and prediction system in an injection molding machine: a case study

Purpose In an increasingly competitive market, equipment availability is a strategic variable for the competitiveness and success of companies. The objective of the research in this article is to present contributions to reduce unplanned production stoppages and optimise the operational efficiency of an injection moulding machine. This will be achieved by developing a systematic strategy to integrate predictive and condition-based maintenance systems with maintenance management software. Design/methodology/approach The model developed is based on the continuous monitoring of electrical signals and vibrations, with the processing of data collected in real time through a script developed in Python. This integrates the information into the maintenance management software, facilitating a quick and accurate response to component wear conditions. The methodology employed was action research, as it was a case study developed in a real context, with active participation in development and implementation, with the aim of continuous improvement. Findings In August, a substantial increase was observed in the primary indicators: The mean time between failures (MTBF) increased by 97.36%, the mean time to repair (MTTR) increased by 313.31%, and the downtime was reduced by 65.04%. In December, although the figures were more moderate, significant improvements were maintained: The MTBF increased by 20%, the MTTR increased by 84%, and the downtime was reduced by 79%. Originality/value The findings of the study indicated that the implementation of a structured approach for the acquisition and monitoring of electrical signals and vibration data was imperative to achieve substantial gains.